Machine learning models may be configured to analyze a predetermined set of labeled training data and then draw certain inferences from the training data. After the model is trained, the model may be fed a different set of data that is not labeled, and make generalizations about each item in the different set of data based on the inferences learned during the training phase.
In some instances, machine learning models may be trained only once based on a particular predetermined set of labeled training data. In other instances, a machine learning model may be an on-line machine learning model that may be periodically updated as new data is received.